CAREER: Towards a Self-Taught Vision System

职业生涯：迈向自学视觉系统

• 批准号: 0546666
• 负责人: Erik Learned-Miller
• 金额: $ 50万
• 依托单位: University of Massachusetts Amherst
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2006
• 资助国家: 美国
• 起止时间: 2006-03-15 至 2012-02-29
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0546666&HistoricalAwards=false
• 关键词: CAREER; Towards; Self; Taught; Vision

Title: CAREER: Towards a Self-Taught Vision SystemPI: Erik Learned-MillerAbstractUsing modern learning techniques, it is now possible to teach computers visual concepts through example-based learning. But this process is time consuming and arduous. Often large data sets must be manually collected. Machines typically do not take advantage of previously learned knowledge when performing new tasks. And when confronted with a new situation, systems fail catastrophically. The goal of this research is to make it dramatically easier to teach vision systems new skills, and to design machines that can learn tasks faster by leveraging previously learned knowledge. In short, the aim is to develop computer vision systems that are largely self-taught. More specifically, this research will focus on problems such as learning from a small number of examples; using previously learned knowledge to improve performance on novel tasks; learning properties of one object that can be used to make inferences about other objects; acquiring and organizing information autonomously; and leveraging interdisciplinary techniques to help relieve people from the burden of ``training'' computers.These capabilities are taken for granted in human beings, but represent serious shortcomings in today's computer systems. A central tenet of this work is that it is impractical to train vision systems one problem at a time, acquiring large training sets and developing training paradigms for each task to be learned. There are many scenarios in which training data are severely limited (there are limited photos of Abraham Lincoln). And ideally, computer systems should be adaptive, and not have to be prepared for each new task, especially when these new tasks are similar to previous ones. Some specific areas of investigation include learning to recognize any particular car or face from a single example, simply by watching other cars or faces as they move about; developing software for robots to continously explore the visual world and the interactions between vision and the other senses; and learning to recognize typewritten text in a font never seen before, without ANY training examples of that font. The common thread in these efforts is that they relieve the burden on the teacher of the computer. The final goal is to develop computers that can be taught simply and rapidly, and that can explore on their own.Educational initiatives will be developed in two areas. The first area is minority and low-income outreach, involving a group of students at an urban Massachusetts school. The second area involves curriculum development and curriculum guidance at the college and graduate levels at UMass, Amherst.Project web page: http://www.cs.umass.edu/~elm/CAREER

职务名称：职业：利用现代学习技术，现在可以通过基于示例的学习来教授计算机视觉概念。 但这一过程既费时又费力。通常，大型数据集必须手动收集。机器在执行新任务时通常不会利用先前学习的知识。当面对新的情况时，系统会灾难性地失败。 这项研究的目标是使视觉系统更容易教授新技能，并设计出能够利用以前学到的知识更快地学习任务的机器。简而言之，我们的目标是开发在很大程度上自学的计算机视觉系统。更具体地说，这项研究将集中在一些问题上，如从少量的例子中学习;使用以前学到的知识来提高新任务的性能;学习一个对象的属性，可以用来推断其他对象;自主获取和组织信息;以及利用跨学科技术帮助人们从“训练”计算机的负担中解脱出来。这些能力在人类身上被认为是理所当然的，但是在当今的计算机系统中代表了严重的缺陷。这项工作的一个核心原则是，一次训练一个问题的视觉系统是不切实际的，需要获得大量的训练集，并为每个要学习的任务开发训练范例。在许多场景中，训练数据非常有限（亚伯拉罕林肯的照片有限）。理想情况下，计算机系统应该是自适应的，不必为每个新任务做好准备，特别是当这些新任务与以前的任务相似时。 一些特定的研究领域包括学习从一个例子中识别任何特定的汽车或人脸，只需观察其他汽车或人脸移动;开发软件让机器人不断探索视觉世界以及视觉和其他感官之间的相互作用;学习识别以前从未见过的字体的打字文本，没有任何该字体的训练样本。 这些努力的共同点是，它们减轻了计算机教师的负担。最终目标是开发出既能简单快速地进行教学，又能自主探索的计算机。 第一个领域是少数族裔和低收入群体的外联活动，涉及马萨诸塞州一所城市学校的一群学生。 第二个领域涉及马萨诸塞大学阿默斯特分校的大学和研究生课程开发和课程指导。项目网页：http://www.cs.umass.edu/~elm/CAREER